0f75d6bb7b
Third and final FRD backward stage. Closes the chain from
softmax+CE loss back to the encoder's hidden state h_t.
Kernel `cuda/rl_frd_layer1_bwd.cu`:
* grid_dim = (B, 1, 1), block_dim = (HIDDEN_DIM=128, 1, 1)
* Phase 0: threads 0..63 stage dL/dpre_hidden = grad_hidden ×
1{hidden > 0} into shared mem (the cached post-ReLU `hidden`
buffer encodes the mask — hidden == 0 ⇔ pre-activation was
≤ 0 → ReLU killed it). Same thread also writes db1_per_batch.
* Phase 1: each thread k (k < 128) writes one row of
grad_W1_per_batch[b, k, 0..64] (64 writes per thread, no atomics)
* Phase 2: same thread computes grad_h_t[b, k] =
Σ_i W1[k, i] × dL/dpre_hidden[b, i]
* Per-(b, k, i) sole-writer per feedback_no_atomicadd
Rust wiring `FrdHead::layer1_bwd` — takes h_t, hidden (forward cache),
grad_hidden (from layer2_bwd), self.w1_d; writes grad_w1_per_batch,
grad_b1_per_batch, grad_h_t. The grad_h_t buffer becomes the encoder-
upstream gradient that the trainer's grad_h_accumulate kernel folds
into the encoder's gradient with λ_frd scaling (same pattern as Q/π/V
heads — wiring lives in F.4).
Tests (2 new, 10/10 file total):
* frd_layer1_bwd_finite_diff_w1 — perturbs the W1 slot with MAX
|analytical gradient| (instead of an arbitrary fixed slot — fp32
finite-diff is rounding-error-limited so a tiny gradient gives
misleading rel_err). At max-magnitude slot (k=84, i=55): analytical
= -0.0451, numerical = -0.0448, rel_err = 5.6e-3 — well within
1e-2 tolerance (slightly looser than dW2's 5e-3 because dW1
crosses an extra matmul + the ReLU mask boundary).
* frd_layer1_bwd_relu_mask_zeros_grad — fixture with h_t = all -1
produces ~half the hidden slots ReLU-masked (cached hidden = 0).
For every masked slot i, asserts:
* db1_per_batch[b, i] == 0 (exact equality — mask is hard 0)
* dW1_per_batch[b, k, i] == 0 for every k (~32 × 128 = 4096
slots checked)
Empirically 32/64 masked, 32/64 active — confirms ReLU mask
is wired through the chain correctly without leaking gradient
through dead branches.
F.3 backward chain is now complete end-to-end:
rl_frd_softmax_ce_grad (F.3a) → rl_frd_layer2_bwd (F.3b) →
rl_frd_layer1_bwd (F.3c) → grad_h_t (consumed by F.4 wiring)
F.4 wires Adam optimizers for W1/b1/W2/b2 + grad_h_accumulate into
the encoder gradient + loader-side label generation + λ_frd × CE
into stats.l_total.
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.